Tasks Scheduling in Cloud environment using PSO-BATS

Abstract Cloud computing plays an important role in the IT industry to deliver scalable resources as a service. Cloud users can virtually access resources over the internet. One of the most important factor to increase the performance and efficiency of the cloud server by maximizing resource utilization as task scheduling. The performance of the cloud mainly depends on resource management and optimally job scheduling. Task scheduling is a technique that defines the choosing of most appropriate resources for the execution of tasks which means the distribution of tasks on available resources by taking some parameters. The main advantage of this scheduling is to maximize the performance and minimize the time loss. There are various researchers are examined numerous scheduling methods to achieve QoS (Quality of Service) and to reduce execution time. However, it had the disadvantages of low throughput and high response time. The main objective of the proposed system is to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the VMs. This research proposed the novel PSO-BATS with MLRHE (Multi-Layered Regression Host Employment) to sort out the issues of task scheduling and ease the scheduling operation with balancing of the load. The proposed efficient scheduling provides benefits to both cloud users and servers. The performance evaluation was done in terms of Makespan, cost, and PIR and found that the proposed method surpassed the other taken methods. In a comparative analysis, the proposed method proved that the best performance compared to existing methods.